Learning the R Tidyverse

Introduction

• Getting started in the R tidyverse
• How to use the exercise files

1. Tidyverse Fundamentals

• What is the tidyverse?
• Installing, loading, and working with the tidyverse packages
• Introducing data.frame and tibbles
• What are %>% and |> for in the tidyverse
• Using the %>% pipe in your code
• Using the |> pipe in your code
• Datasets built into the tidyverse packages
• Using the select() function to obtain columns from data
• Using the filter() function to filter data by conditions
• Using the mutate() function to modify and add columns
• Challenge: Rewrite this code to use the pipe of your choice
• Solution: Rewrite this code to use the pipe of your choice

2. Tidy Data: The Fundamental Idea behind the Tidyverse

• What is tidy data?
• Why does ggplot2 want tidy data?
• Using pivot_longer() to tidy data into a long format
• Cleaning column names with the janitor package
• Tidying columns containing multiple values with separate_*()
• List columns and nested tibbles

3. Reading Data In and Out of the Tidyverse

• Using projects to simplify file paths
• Using read_csv() to read CSV files
• Using read_excel() to read data from Excel files
• Using haven to import from SPSS and other formats

4. Grouping and Summarizing Data with the Tidyverse

• Grouping and summarizing data by column or row
• Cross tabulations with count()
• Column-wise groups: group_by() and mutate()
• Column-wise groups: group_by() and summarize()
• Column-wise groups: group_by() and reframe()
• Column-wise groups: Using the .by argument instead of group_by()
• Row-wise groups: rowwise() and c_across()
• Remember to ungroup()
• Challenge: Find maximum penguin dimension by island
• Solution: Find maximum penguin dimension by island

5. Important Packages and Functions in the Tidyverse

• ggplot2 for beautiful data storytelling
• stringr for friendly string manipulation
• lubridate for manipulating dates and times
• forcats for manipulating factors
• purrr for doing many things like iteration

6. Working Smart with the Tidyverse

• Handling NAs in the tidyverse with drop_na() and replace_na()
• Use case_when() instead of nested if or ifelse()
• Use tidy-select functions to work with many columns at once
• Using across() in mutate() to modify multiple columns at once
• Filtering many columns at once with if_any() and if_all()
• Understanding how the tidyverse evolves and deprecates
• Challenge: Find all love songs remaining below position 80 in the top 10
• Solution: Find all love songs remaining below position 80 in the top 10

Conclusion

• Next steps